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Fraud	Demo	
Solu%ons	powered	with	Neo4j		
2015	
Stefan	Kolmar	
Neo	Technologies
Retail	Banking	First-Party	Fraud	
Opening	many	lines	of	credit	with	no	inten5on	of		
paying	them	back	
Causing	High	Impact	
•  Tens	of	billions	of	dollars	
lost	every	year	by	
U.S.	Banks.(1)	
•  25%	of	total	consumer	
credit	charge-offs	in	the	
United	States.(2)	
•  10%	to	20%	of	unsecured	
bad	debt	at	leading		
U.S.	and	European	banks	
is	misclassified,	and		
is	actually	first-party	fraud.(3)	
	
(1)		Experian:																															hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf	
(2)		Experian:																															hQp://www.experian.com/assets/decision-analy5cs/white-papers/first-partyfraud-wp.pdf	
(3)		Business	Insider:																		hQp://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3
Detec%ng	Fraud	Rings	
SSN1	
123	NW	
1st	Street	
San	
Francisco,	
CA	
555-5
55-55
55	
123	NW	
1st	Street	
San	
Francisco,	
CA	555-5
55-55
55	
Skimming	
Person	A	 Person	B	
Loca5on	A	 Loca5on	B	
Phone	Number	Duplicate	Use	
555-555-5
555	
Person	A	
Person	B	
Suspect	eCommerce	
Person	A	
Person	B	
Loca5on	C	
IP	address
Fraud	Demo	–	Part	I	(generic)	
•  Fraud	scenario	covering	Retail	Fraud	use	cases	
•  Data	set	contains	opera5onal	data	
•  Constant	data	load	–>	injec5ng	fraud	cases	->	generate	alerts	
•  Capability	to	export	data	of	detected	fraud	for	further	inves5ga5on	
Neo4j	
App	Server	
Fraud	
Detec5on	
Web	App	
Fraud	App	
Browser	
UX:	
TestDataGen	
Alert	generated
Demo
Why	using	GraphDB	/	Neo4j	for	Fraud?	
•  Agile	Development	
•  High	produc5vity	and	rapid	implementa5on		
•  No	“RDBMS-waterfall-high-investment-trap”		
•  Taking	advantage	of	the	full	value	of	connected	data	
•  Traversing	the	graph	compared	to	self	joins	in	RDBMS	
•  Near	real	5me	response	5mes	
•  Preven5ng	fraud	rather	than	detec5ng	ader	the	fact	
•  Schema	free		
•  Nodes	can	vary	depending	on	5me	/	usage	/	seman5c
•  Usage	scenario	Fraud	Analyst:		
•  Poten5al	fraud	case	detected	
•  Enriched	with	data	from	various	sources	containing	data	on	fraud	suspect	
•  Trigger	human	and/or	automated	reac5ons	
Fraud	Demo	–	Part	II	
Neo4j	
Web	
App	 Data	
Integra5on	
RDBMS	
(Oracle,	MySQL,	
DB2,	HANA	…)	
Management	Console	
(E.g	BI	Tools	such	as		
Tableau,	Qlik,	BO,	
MicroStrategy	etc)	
Fraud	
Analyst	
Machine2Machine		
generated	ac5ons	
Alert	
Incoming	Events	
CRM	System	
	
	
	
	
	
Opera5onal	System	
	
	
External	Data
Using	Neo	as	the	founda%on	of	a	fraud	
solu%on	in	your	architecture	
Step	1:	Set	up	Data	Integra5on	
Step	2:	Visualize	Data	in	BI	Tool
Conclusions	
•  Fraud	as	one	use	case	to	provide	full	value	of	connected	data	within	
the	en5re	organiza5on	
•  Neo4j	as	the	founda5on	to	do	360	degree	fraud	detec5on	and	
preven5on	
•  Neo4j	to	extend	your	exis5ng	environment	while	protec5ng	your	
investments	
•  Neo4j	provides	best	value	integrated	in	the	en5re	environment	
•  Neo4j	as	the	founda5on	for	genera5ng	real	5me	alerts	to	trigger	
automated	or	manual	interven5ons
Encore
…	One	more	thing	...
A	brief	look	into	the	data	model	….
Fraud	Demo	
Solu%ons	powered	with	Neo4j		
2015	
	
Stefan.Kolmar@neotechnology.com

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